{"id":"W3144134537","doi":"10.47912/jscdm.29","title":"Electronic Data Capture-Selecting an EDC System","year":2020,"lang":"en","type":"article","venue":"Journal of the Society for Clinical Data Management","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Inflamax Research (Canada)","funders":"","keywords":"Electronic data capture; Computer science; Process (computing); Process management; Selection (genetic algorithm); Key (lock); Automatic identification and data capture; Software; Electronic data; Systems engineering; Software engineering; Data collection; Database; Engineering; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02332524,0.000583037,0.0006755466,0.003137565,0.001545776,0.005036622,0.001610274,0.001527077,0.02746989],"category_scores_gemma":[0.04299568,0.0007248465,0.0006282907,0.002559572,0.000446393,0.00346421,0.002352657,0.001071652,0.02096556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001878585,"about_ca_system_score_gemma":0.003673399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001981133,"about_ca_topic_score_gemma":0.002271746,"domain_scores_codex":[0.9848663,0.005770576,0.0018374,0.001070739,0.005866499,0.0005884974],"domain_scores_gemma":[0.9634681,0.01459047,0.001412925,0.002524855,0.01641054,0.001593197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008565956,0.0003551316,0.02374894,0.002577001,0.00005060577,0.001048701,0.00212624,0.0008993244,0.02028742,0.0166592,0.21684,0.7145507],"study_design_scores_gemma":[0.0003158572,0.0008332728,0.02967757,0.0033579,0.0001305488,0.002566451,0.003437603,0.005967781,0.04790837,0.006637663,0.8989028,0.0002641377],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06766881,0.005388913,0.5382958,0.02033686,0.002521806,0.02834882,0.01404383,0.01816739,0.3052278],"genre_scores_gemma":[0.1359527,0.005041489,0.7496446,0.009431091,0.001012685,0.01080129,0.01008746,0.002956827,0.07507186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02746989,"threshold_uncertainty_score":0.1233572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3715713256232152,"score_gpt":0.4163583071334349,"score_spread":0.04478698151021976,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}